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<title>Multi-omics</title>





  
  

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          <a href="conversion.html">Converting VoltRon Objects</a>





  
  

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          <a href="ondisk.html">OnDisk-based Analysis Utilities</a>





  
  

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<h1 class="title toc-ignore">Multi-omics</h1>





  
  

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</div>





  
  

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<p><br></p>





  
  

  467
  
<div id="label-transfer-via-registration" class="section level1">





  
  

  468
  
<h1>Label Transfer via Registration</h1>





  
  

  469
  
<p>VoltRon is capable of analyzing molecule-level subcellular datasets





  
  

  470
  
independent of single cells, and specifically those that may also found





  
  

  471
  
outside of these cells. To demonstrate how VoltRon investigates





  
  

  472
  
extra-cellular molecules, we will make use of another Xenium in situ





  
  

  473
  
dataset where custom Xenium probes designed to hybridize distinct open





  
  

  474
  
reading frames of SARS-COV-2 virus molecules. These subcellular entities





  
  

  475
  
which then can be detected both within and outside of cells which allows





  
  

  476
  
to understand proliferation mechanics of the virus across the





  
  

  477
  
tissue.</p>





  
  

  478
  
<p>In this use case, we analyse readouts of <strong>eight tissue micro





  
  

  479
  
array (TMA) tissue sections</strong> that were fitted into the scan area





  
  

  480
  
of a slide loaded on a Xenium in situ instrument. These sections were





  
  

  481
  
cut from <strong>control and COVID-19 lung tissues</strong> of donors





  
  

  482
  
categorized based on disease durations (acute and prolonged). You can





  
  

  483
  
download the standard Xenium output folder <a





  
  

  484
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Multiomics/Xenium_SARSCOV2.zip">here</a>.</p>





  
  

  485
  
<p>For more information on the TMA blocks, see <a





  
  

  486
  
href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190732">GSE190732</a>





  
  

  487
  
for more information.</p>





  
  

  488
  
<p><br></p>





  
  

  489
  
<div id="single-cells-and-molecules" class="section level2">





  
  

  490
  
<h2>Single Cells and Molecules</h2>





  
  

  491
  
<p>Across these eight TMA section, we investigate a section of acute





  
  

  492
  
case which is originated from a lung with extreme number of detected





  
  

  493
  
open reading frames of virus molecules. For convenience, we load a





  
  

  494
  
VoltRon object where cells are already annotated. You can also find the





  
  

  495
  
RDS file <a





  
  

  496
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Multiomics/acutecase1_annotated.rds">here</a>.</p>





  
  

  497
  
<pre class="r watch-out"><code>vr2_merged_acute1 &lt;- readRDS(file = &quot;acutecase1_annotated.rds&quot;)</code></pre>





  
  

  498
  
<p><br></p>





  
  

  499
  
<p>Lets visualize both the UMAP and Spatial plot of the annotated





  
  

  500
  
cells.</p>





  
  

  501
  
<pre class="r watch-out"><code>vrEmbeddingPlot(vr2_merged_acute1, assay = &quot;Xenium&quot;, embedding = &quot;umap&quot;, 





  
  

  502
  
                group.by = &quot;CellType&quot;, label = TRUE)





  
  

  503
  
vrSpatialPlot(vr2_merged_acute1, assay = &quot;Xenium&quot;, group.by = &quot;CellType&quot;,





  
  

  504
  
              plot.segments = TRUE)</code></pre>





  
  

  505
  
<table>





  
  

  506
  
<tbody>





  
  

  507
  
<tr style="vertical-align: center">





  
  

  508
  
<td style="width:45%; vertical-align: center">





  
  

  509
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_vrembeddingplot.png" class="center">





  
  

  510
  
</td>





  
  

  511
  
<td style="width:55%; vertical-align: center">





  
  

  512
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_visualize_celltype.png" class="center">





  
  

  513
  
</td>





  
  

  514
  
</tr>





  
  

  515
  
</tbody>





  
  

  516
  
</table>





  
  

  517
  
<p><br></p>





  
  

  518
  
<p>We incorporate two open reading frames (ORFs), named





  
  

  519
  
<strong>S2_N</strong> and <strong>S2_orf1ab</strong>, which represent





  
  

  520
  
unreplicated and replicated virus molecules, respectively. We can





  
  

  521
  
visualize again tile the count of these virus expressions by





  
  

  522
  
specifically selecting these two ORFs.</p>





  
  

  523
  
<pre class="r watch-out"><code>vrSpatialPlot(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;, group.by = &quot;gene&quot;, 





  
  

  524
  
              group.ids = c(&quot;S2_N&quot;, &quot;S2_orf1ab&quot;), n.tile = 500)</code></pre>





  
  

  525
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_visualize_virus.png" class="center"></p>





  
  

  526
  
<p><br></p>





  
  

  527
  
<p>We can even zoom into specific locations at the tissue to investigate





  
  

  528
  
virus particles more closely by droping the <strong>n.tile</strong>





  
  

  529
  
argument and calling interactive visualization.</p>





  
  

  530
  
<pre class="r watch-out"><code>vrSpatialPlot(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;, group.by = &quot;gene&quot;, 





  
  

  531
  
              group.ids = c(&quot;S2_N&quot;, &quot;S2_orf1ab&quot;), interactive = TRUE, pt.size = 0.1)</code></pre>





  
  

  532
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecules_visualize_virus_zoom.png" class="center"></p>





  
  

  533
  
<p><br></p>





  
  

  534
  
<p>In the following sections, we will integrate pathological images and





  
  

  535
  
annotations with Xenium datasets to further understand the spatial





  
  

  536
  
localization of virus ORF types over the tissue.</p>





  
  

  537
  
<p><br></p>





  
  

  538
  
</div>





  
  

  539
  
<div id="hot-spot-analysis" class="section level2">





  
  

  540
  
<h2>Hot Spot Analysis</h2>





  
  

  541
  
<p>VoltRon platform allows users to find hot spots of several types of





  
  

  542
  
spatial entities, for spots, cells, and even molecules. We first learn





  
  

  543
  
spatial neighborhoods from molecules of interests, in this case, the





  
  

  544
  
extracellular virus particles and their ORFs.</p>





  
  

  545
  
<pre class="r watch-out"><code>vr2_merged_acute1</code></pre>





  
  

  546
  
<pre><code>VoltRon Object 





  
  

  547
  
acute case 1: 





  
  

  548
  
  Layers: Section1 





  
  

  549
  
Assays: Xenium(Main) Xenium_mol </code></pre>





  
  

  550
  
<p>We switch to the molecule assay of the VoltRon object, and select





  
  

  551
  
virus ORFs. We also look for other virus ORFs that are 50 pixel distance





  
  

  552
  
from each virus molecule to pin point neighborhoods that are rich in





  
  

  553
  
virus.</p>





  
  

  554
  
<pre class="r watch-out"><code>vr2_merged_acute1 &lt;- getSpatialNeighbors(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;, 





  
  

  555
  
                                         group.by = &quot;gene&quot;, group.ids = c(&quot;S2_N&quot;, &quot;S2_orf1ab&quot;), 





  
  

  556
  
                                         method = &quot;radius&quot;, radius = 50)</code></pre>





  
  

  557
  
<p>We can now observe the new spatial neighborhood graph saved in the





  
  

  558
  
VoltRon object with name <strong>radius</strong>.</p>





  
  

  559
  
<pre class="r watch-out"><code>vrGraphNames(vr2_merged_acute1)</code></pre>





  
  

  560
  
<pre><code>[1] &quot;radius&quot;</code></pre>





  
  

  561
  
<p>We now select the feature type and graph name to run an hot spot





  
  

  562
  
analysis which will label each molecule if their neighborhood are dense





  
  

  563
  
in other virus molecules.</p>





  
  

  564
  
<pre class="r watch-out"><code>vr2_merged_acute1 &lt;- getHotSpotAnalysis(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;, 





  
  

  565
  
                                        features = &quot;gene&quot;, graph.type = &quot;radius&quot;)





  
  

  566
  
vrSpatialPlot(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;, 





  
  

  567
  
              group.by = &quot;gene_hotspot_flag&quot;, group.ids = c(&quot;cold&quot;, &quot;hot&quot;), 





  
  

  568
  
              alpha = 1, background.color = &quot;white&quot;)</code></pre>





  
  

  569
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_visualize_virus_hotspot.png" class="center"></p>





  
  

  570
  
</div>





  
  

  571
  
<div id="automated-he-registration" class="section level2">





  
  

  572
  
<h2>Automated H&amp;E Registration</h2>





  
  

  573
  
<p>VoltRon can analyze and also integrate information from distinct





  
  

  574
  
spatial data types such as images, annotations (as regions of interests,





  
  

  575
  
i.e. ROIs) and molecules independently. Using such advanced utilities,





  
  

  576
  
we can make use of histological information and generate new metadata





  
  

  577
  
level information for molecule datasets.</p>





  
  

  578
  
<p>We will first import both histological images and manual annotations





  
  

  579
  
using the <strong>importImageData</strong> function which accepts both





  
  

  580
  
images to generate tile/pixel level datasets but also allows one to





  
  

  581
  
import either a list of segments or <a





  
  

  582
  
href="https://geojson.org/">GeoJSON</a> objects for create ROI-level





  
  

  583
  
datasets as separate assays in a single VoltRon layer. The .geojson file





  
  

  584
  
was generated using <a href="https://qupath.github.io/">QuPath</a> on





  
  

  585
  
the same section H&amp;E image of one Xenium section with the acute





  
  

  586
  
COVID-19 case. We also have to flip the coordinates of ROI annotations





  
  

  587
  
also for they were directly imported from QuPath which incorporates a





  
  

  588
  
reverse coordinate system on the y-axis.</p>





  
  

  589
  
<p>Once imported, the resulting VoltRon object will have two assays in a





  
  

  590
  
single layer, one for tile dataset of the H&amp;E image and the other





  
  

  591
  
for ROI based annotations of again the same image. You can download the





  
  

  592
  
H&amp;E image from <a





  
  

  593
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Multiomics/acutecase1_HE.jpg">here</a>,





  
  

  594
  
and download the json file from <a





  
  

  595
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Multiomics/acutecase1_membrane.geojson">here</a>.</p>





  
  

  596
  
<pre class="r watch-out"><code># get image





  
  

  597
  
imgdata &lt;- importImageData(&quot;acutecase1_HE.jpg&quot;, 





  
  

  598
  
                           segments = &quot;acutecase1_membrane.geojson&quot;, 





  
  

  599
  
                           sample_name = &quot;acute case 1 (HE)&quot;)





  
  

  600
  
imgdata &lt;- flipCoordinates(imgdata, assay = &quot;ROIAnnotation&quot;)





  
  

  601
  
imgdata</code></pre>





  
  

  602
  
<pre><code>VoltRon Object 





  
  

  603
  
acute case 1 (HE): 





  
  

  604
  
  Layers: Section1 





  
  

  605
  
Assays: ImageData(Main) ROIAnnotation 





  
  

  606
  
Features: main(Main) </code></pre>





  
  

  607
  
<p>By visualizing these annotations interactively, we can see that the





  
  

  608
  
ROIs point to the hyaline membranes. Here, we likely find extra-cellular





  
  

  609
  
SARS-COV-2 molecules mostly outside of any single cell.</p>





  
  

  610
  
<pre class="r watch-out"><code>vrSpatialPlot(imgdata, assay = &quot;ROIAnnotation&quot;, group.by = &quot;Sample&quot;, 





  
  

  611
  
              alpha = 0.7, interactive = TRUE)</code></pre>





  
  

  612
  
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_Covid_HE_zoom.png" class="center"></p>





  
  

  613
  
<p><br></p>





  
  

  614
  
<p>At a first glance, although the Xenium (DAPI) and H&amp;E images are





  
  

  615
  
associated with the same TMA core, they were captured in a different





  
  

  616
  
perspective; that is, one image is almost the 90 degree rotated version





  
  

  617
  
of the other. We will account for this rotation when we (automatically)





  
  

  618
  
align the Xenium data with the H&amp;E image.</p>





  
  

  619
  
<pre class="r watch-out"><code>vr2_merged_acute1 &lt;- modulateImage(vr2_merged_acute1, brightness = 300, channel = &quot;DAPI&quot;)





  
  

  620
  
vrImages(vr2_merged_acute1, assay = &quot;Assay7&quot;, scale.perc = 20)





  
  

  621
  
vrImages(imgdata, assay = &quot;Assay1&quot;, scale.perc = 20)</code></pre>





  
  

  622
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/multiomic_twoimages.png" class="center"></p>





  
  

  623
  
<p><br></p>





  
  

  624
  
<p>We now register the H&amp;E image and annotations to the DAPI image





  
  

  625
  
of Xenium section using the <strong>registerSpatialData</strong>





  
  

  626
  
function. We select FLANN (with “Homography” method) automated





  
  

  627
  
registration mode, negate the DAPI image of the Xenium slide, turn 90





  
  

  628
  
degrees to the left and set the scale parameter of both images to





  
  

  629
  
<strong>width = 1859</strong>. See <a href="registration.html">Spatial





  
  

  630
  
Data Alignment</a> tutorial for more information.</p>





  
  

  631
  
<pre class="r watch-out"><code>xen_reg &lt;- registerSpatialData(object_list = list(vr2_merged_acute1, imgdata))</code></pre>





  
  

  632
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_HE_registration.png" class="center"></p>





  
  

  633
  
<p><br></p>





  
  

  634
  
<p>Once registered, we can isolate the registered H&amp;E data and use





  
  

  635
  
it further analysis. We can transfer images and their channels across





  
  

  636
  
assays of one or multiple VoltRon objects. Here, we save the registered





  
  

  637
  
image of the H&amp;E data as an additional channel of Xenium section of





  
  

  638
  
the acuse case 1 sample with molecule data.</p>





  
  

  639
  
<pre class="r watch-out"><code>imgdata_reg &lt;- xen_reg$registered_spat[[2]]





  
  

  640
  
vrImages(vr2_merged_acute1[[&quot;Assay7&quot;]], name = &quot;main&quot;, channel = &quot;H&amp;E&quot;) &lt;- 





  
  

  641
  
  vrImages(imgdata_reg, assay = &quot;Assay1&quot;, name = &quot;main_reg&quot;)





  
  

  642
  
vrImages(vr2_merged_acute1[[&quot;Assay8&quot;]], name = &quot;main&quot;, channel = &quot;H&amp;E&quot;) &lt;- 





  
  

  643
  
  vrImages(imgdata_reg, assay = &quot;Assay1&quot;, name = &quot;main_reg&quot;)</code></pre>





  
  

  644
  
<p>We can now observe the new channels available for the both molecule





  
  

  645
  
and cell-level assays of Xenium data.</p>





  
  

  646
  
<pre class="r watch-out"><code>vrImageChannelNames(vr2_merged_acute1)</code></pre>





  
  

  647
  
<pre><code>               Assay    Layer       Sample  Spatial Channels





  
  

  648
  
Assay7        Xenium Section1 acute case 1     main DAPI,H&amp;E





  
  

  649
  
Assay8    Xenium_mol Section1 acute case 1     main DAPI,H&amp;E</code></pre>





  
  

  650
  
<p><br></p>





  
  

  651
  
<p>You can also add the VoltRon object of H&amp;E data as an additional





  
  

  652
  
assay of the Xenium section such that one layer includes cell,





  
  

  653
  
molecules, ROI Annotations and images in the same time. Specifically, we





  
  

  654
  
add the ROI annotation to the Xenium VoltRon object using the





  
  

  655
  
<strong>addAssay</strong> function where we choose the destination





  
  

  656
  
sample/block and the layer of the assay.</p>





  
  

  657
  
<pre class="r watch-out"><code>vr2_merged_acute1 &lt;- addAssay(vr2_merged_acute1,





  
  

  658
  
                              assay = imgdata_reg[[&quot;Assay2&quot;]],





  
  

  659
  
                              metadata = Metadata(imgdata_reg, assay = &quot;ROIAnnotation&quot;),





  
  

  660
  
                              assay_name = &quot;ROIAnnotation&quot;,





  
  

  661
  
                              sample = &quot;acute case 1&quot;, layer = &quot;Section1&quot;)





  
  

  662
  
vr2_merged_acute1</code></pre>





  
  

  663
  
<pre><code>VoltRon Object 





  
  

  664
  
acute case 1: 





  
  

  665
  
  Layers: Section1 





  
  

  666
  
Assays: ROIAnnotation(Main) Xenium_mol Xenium </code></pre>





  
  

  667
  
<p><br></p>





  
  

  668
  
</div>





  
  

  669
  
<div id="interactive-visualization" class="section level2">





  
  

  670
  
<h2>Interactive Visualization</h2>





  
  

  671
  
<p>Once the H&amp;E image is registered and transfered to the Xenium





  
  

  672
  
data, we can convert the VoltRon object into an Anndata object (h5ad





  
  

  673
  
file) and use <a href="https://tissuumaps.github.io/">TissUUmaps</a>





  
  

  674
  
tool for interactive visualization.</p>





  
  

  675
  
<pre class="r watch-out"><code># convert VoltRon object to h5ad





  
  

  676
  
as.AnnData(vr2_merged_acute1, assay = &quot;Xenium&quot;, file = &quot;vr2_merged_acute1.h5ad&quot;, 





  
  

  677
  
           flip_coordinates = TRUE, name = &quot;main&quot;, channel = &quot;H&amp;E&quot;)</code></pre>





  
  

  678
  
<p>To run TissUUmaps please follow installation instructions <a





  
  

  679
  
href="https://tissuumaps.github.io/installation/">here</a>, then you can





  
  

  680
  
simply drag and drop both the h5ad file and png file to the





  
  

  681
  
application.</p>





  
  

  682
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/multiomic_interactivevisualization.png" class="center"></p>





  
  

  683
  
<p><br></p>





  
  

  684
  
</div>





  
  

  685
  
<div id="label-transfer" class="section level2">





  
  

  686
  
<h2>Label transfer</h2>





  
  

  687
  
<p>Now we can transfer ROI annotations as additional metadata features





  
  

  688
  
of the molecule assay. We will refer the new metadata column as “Region”





  
  

  689
  
which will indicate if the molecule is within any annotated ROI in the





  
  

  690
  
same layer.</p>





  
  

  691
  
<pre class="r watch-out"><code>vrMainAssay(vr2_merged_acute1) &lt;- &quot;ROIAnnotation&quot;





  
  

  692
  
vr2_merged_acute1$Region &lt;- vrSpatialPoints(vr2_merged_acute1)





  
  

  693
  





  
  

  694
  
# set the spatial coordinate system of ROI Annotations assay





  
  

  695
  
vrMainSpatial(vr2_merged_acute1[[&quot;Assay9&quot;]]) &lt;- &quot;main_reg&quot;





  
  

  696
  





  
  

  697
  
# transfer ROI annotations to molecules





  
  

  698
  
vr2_merged_acute1 &lt;- transferData(object = vr2_merged_acute1, from = &quot;Assay9&quot;, to = &quot;Assay8&quot;, 





  
  

  699
  
                                  features = &quot;Region&quot;)





  
  

  700
  





  
  

  701
  
# Metadata of molecules





  
  

  702
  
Metadata(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;)</code></pre>





  
  

  703
  
<div>





  
  

  704
  
<pre><code style="font-size: 11px;">                            id assay_id overlaps_nucleus   gene       qv      Assay    Layer       Sample    Region





  
  

  705
  
                        &lt;char&gt;   &lt;char&gt;            &lt;int&gt; &lt;char&gt;    &lt;num&gt;     &lt;char&gt;   &lt;char&gt;       &lt;char&gt;    &lt;char&gt;





  
  

  706
  
     1: 281651070371256_cb791e   Assay8                0   ENAH 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  707
  
     2: 281651070371258_cb791e   Assay8                1  CD274 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  708
  
     3: 281651070372515_cb791e   Assay8                0  CD163 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  709
  
     4: 281651070374059_cb791e   Assay8                1   CTSL 33.97290 Xenium_mol Section1 acute case 1 undefined





  
  

  710
  
     5: 281651070374411_cb791e   Assay8                0    C1S 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  711
  
    ---                                                                                                            





  
  

  712
  
785787: 281874408669584_cb791e   Assay8                0  SFRP2 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  713
  
785788: 281874408671410_cb791e   Assay8                0   S2_N 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  714
  
785789: 281874408672438_cb791e   Assay8                0 S100A8 40.00000 Xenium_mol Section1 acute case 1 undefined





  
  

  715
  
785790: 281874408673446_cb791e   Assay8                0  TIMP1 29.86642 Xenium_mol Section1 acute case 1 undefined





  
  

  716
  
785791: 281874408673882_cb791e   Assay8                0   S2_N 40.00000 Xenium_mol Section1 acute case 1 undefined</code></pre>





  
  

  717
  
</div>





  
  

  718
  
<p><br></p>





  
  

  719
  
<p>This annotations can be accessed from the default molecule level





  
  

  720
  
metadata of the VoltRon object.</p>





  
  

  721
  
<pre class="r watch-out"><code>vrMainAssay(vr2_merged_acute1) &lt;- &quot;Xenium_mol&quot;





  
  

  722
  
head(table(vr2_merged_acute1$Region))</code></pre>





  
  

  723
  
<pre><code>  ROI1_Assay9  ROI10_Assay9 ROI100_Assay9 ROI101_Assay9 ROI102_Assay9 ROI103_Assay9 





  
  

  724
  
          583           624           784           357           215           200 </code></pre>





  
  

  725
  
<p><br></p>





  
  

  726
  
<p>Now we will grab these annotations from molecule metadata and





  
  

  727
  
calculate the ratio of N to orf1ab frequency of SARS-COV-2 particles





  
  

  728
  
across all annotated molecules.</p>





  
  

  729
  
<pre class="r watch-out"><code>library(dplyr)





  
  

  730
  
s2_summary_hyaline &lt;- 





  
  

  731
  
  Metadata(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;) %&gt;%





  
  

  732
  
  filter(gene %in% c(&quot;S2_N&quot;, &quot;S2_orf1ab&quot;), 





  
  

  733
  
         Region != &quot;undefined&quot;) %&gt;% 





  
  

  734
  
  summarise(S2_N = sum(gene == &quot;S2_N&quot;), 





  
  

  735
  
            S2_orf1ab = sum(gene == &quot;S2_orf1ab&quot;), 





  
  

  736
  
            ratio = sum(gene == &quot;S2_N&quot;)/sum(gene == &quot;S2_orf1ab&quot;)) %&gt;% 





  
  

  737
  
  as.matrix()





  
  

  738
  
s2_summary_hyaline</code></pre>





  
  

  739
  
<pre><code>      S2_N S2_orf1ab    ratio





  
  

  740
  
[1,] 50977     33532 1.520249</code></pre>





  
  

  741
  
<p><br></p>





  
  

  742
  
</div>





  
  

  743
  
<div id="manual-annotation" class="section level2">





  
  

  744
  
<h2>Manual annotation</h2>





  
  

  745
  
<p>To compare the proportion of <strong>S2_N</strong> and





  
  

  746
  
<strong>S2_orf1ab</strong> molecules in hyaline membranes with tissue





  
  

  747
  
sites of possible infection. We focus on another tissue niche where a





  
  

  748
  
large accumulation of cells with high <strong>S2_N</strong> and





  
  

  749
  
<strong>S2_orf1ab</strong> counts.</p>





  
  

  750
  
<p>By visualizing and zooming on a spatial plot with annotated cells, we





  
  

  751
  
can detect a site of cells with high infection which are also





  
  

  752
  
accompanied by a group of T cells.</p>





  
  

  753
  
<pre class="r watch-out"><code>vrSpatialPlot(vr2_merged_acute1, assay = &quot;Xenium&quot;, group.by = &quot;CellType&quot;, 





  
  

  754
  
              group.ids = c(&quot;H.I. Cells&quot;, &quot;T cells&quot;), plot.segments = TRUE, 





  
  

  755
  
              alpha = 0.6, spatial = &quot;main&quot;, channel = &quot;H&amp;E&quot;, interactive = TRUE)</code></pre>





  
  

  756
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_spatialplot_infected.png" class="center"></p>





  
  

  757
  
<p><br></p>





  
  

  758
  
<p>Now we use the <strong>annotateSpatialData</strong> function to





  
  

  759
  
create an additional annotation of Xenium molecules directly. By





  
  

  760
  
selecting this region of infection we can directly annotate the





  
  

  761
  
‘Xenium_mol’ assay and the metadata of molecules. We use the H&amp;E





  
  

  762
  
image as the background again, and generate a new molecule-level





  
  

  763
  
metadata column called “Infected”.</p>





  
  

  764
  
<pre class="r watch-out"><code>vr2_merged_acute1 &lt;- annotateSpatialData(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;, 





  
  

  765
  
                                         label = &quot;Region&quot;, use.image = TRUE, 





  
  

  766
  
                                         channel = &quot;H&amp;E&quot;, annotation_assay = &quot;ROIAnnotation1&quot;)</code></pre>





  
  

  767
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_annotation_infected.png" class="center"></p>





  
  

  768
  
<p><br></p>





  
  

  769
  
</div>





  
  

  770
  
<div id="visualize-annotations" class="section level2">





  
  

  771
  
<h2>Visualize annotations</h2>





  
  

  772
  
<p>Before visualizing the annotations and molecule localizations in the





  
  

  773
  
same time, lets make some changes to the metadata. We basically wanna





  
  

  774
  
change the annotation of all ROIs originated from the GeoJson file to





  
  

  775
  
have the label <strong>Hyaline Membrane</strong>.</p>





  
  

  776
  
<pre class="r watch-out"><code># update molecule metadata





  
  

  777
  
vrMainAssay(vr2_merged_acute1_infected) &lt;- &quot;Xenium_mol&quot;





  
  

  778
  
vr2_merged_acute1_infected$Region &lt;- gsub(&quot;ROI[0-9]+&quot;, &quot;Hyaline Membrane&quot;, 





  
  

  779
  
                                          vr2_merged_acute1_infected$Region)





  
  

  780
  





  
  

  781
  
# update ROI metadata





  
  

  782
  
vrMainAssay(vr2_merged_acute1_infected) &lt;- &quot;ROIAnnotation&quot;





  
  

  783
  
vr2_merged_acute1_infected$Region &lt;- gsub(&quot;ROI[0-9]+&quot;, &quot;Hyaline Membrane&quot;, 





  
  

  784
  
                                          vr2_merged_acute1_infected$Region)</code></pre>





  
  

  785
  
<p>Now we can visualize two assays together. We use the





  
  

  786
  
<strong>addSpatialLayer</strong> function to overlay molecule locations





  
  

  787
  
with both the Hyaline Membrane and the infected region annotations.</p>





  
  

  788
  
<pre class="r watch-out"><code>vrSpatialPlot(vr2_merged_acute1_infected, assay = &quot;Xenium_mol&quot;, group.by = &quot;gene&quot;, 





  
  

  789
  
              group.ids = c(&quot;S2_N&quot;, &quot;S2_orf1ab&quot;), n.tile = 500) |&gt;





  
  

  790
  
  addSpatialLayer(vr2_merged_acute1_infected, assay = &quot;ROIAnnotation&quot;, 





  
  

  791
  
                  group.by = &quot;Region&quot;, alpha = 0.3, 





  
  

  792
  
                  colors = list(`Hyaline Membrane` = &quot;blue&quot;, `Infected` = &quot;yellow&quot;))</code></pre>





  
  

  793
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/molecule_visualize_virus_overlay.png" class="center"></p>





  
  

  794
  
</div>





  
  

  795
  
<div id="comparison-of-annotations" class="section level2">





  
  

  796
  
<h2>Comparison of annotations</h2>





  
  

  797
  
<p>By using the newly annotated infection-associated virus molecules, we





  
  

  798
  
can do a comparison of infected regions versus the hyaline





  
  

  799
  
membranes.</p>





  
  

  800
  
<pre class="r watch-out"><code>library(dplyr)





  
  

  801
  
s2_summary_infected &lt;- 





  
  

  802
  
  Metadata(vr2_merged_acute1, assay = &quot;Xenium_mol&quot;) %&gt;%





  
  

  803
  
  filter(gene %in% c(&quot;S2_N&quot;, &quot;S2_orf1ab&quot;), 





  
  

  804
  
         Region == &quot;Infected&quot;) %&gt;% 





  
  

  805
  
  summarise(S2_N = sum(gene == &quot;S2_N&quot;), 





  
  

  806
  
            S2_orf1ab = sum(gene == &quot;S2_orf1ab&quot;), 





  
  

  807
  
            ratio = sum(gene == &quot;S2_N&quot;)/sum(gene == &quot;S2_orf1ab&quot;)) %&gt;% 





  
  

  808
  
  as.matrix()





  
  

  809
  
s2_summary_infected</code></pre>





  
  

  810
  
<pre><code>     S2_N S2_orf1ab    ratio





  
  

  811
  
[1,] 6376      2372 2.688027</code></pre>





  
  

  812
  
<p>Comparison of both the ratio between the infected region and the





  
  

  813
  
hyaline membranes show a considerable difference of the population of





  
  

  814
  
<strong>S2_N</strong> and <strong>S2_orf1ab</strong> molecules.</p>





  
  

  815
  
<pre class="r watch-out"><code>S2_table &lt;- matrix(c(s2_summary_hyaline[,1:2], 





  
  

  816
  
                     s2_summary_infected[,1:2]), 





  
  

  817
  
                   dimnames = list(Region = c(&quot;Hyaline&quot;, &quot;Infected&quot;), 





  
  

  818
  
                                   S2 = c(&quot;N&quot;, &quot;orf1ab&quot;)),





  
  

  819
  
                   ncol = 2,  byrow = TRUE)





  
  

  820
  
S2_table</code></pre>





  
  

  821
  
<pre><code>          S2





  
  

  822
  
Region     N     orf1ab





  
  

  823
  
  Hyaline  50977 33532 





  
  

  824
  
  Infected 6376  2372</code></pre>





  
  

  825
  
<p>A quick test of independance on this contingency table show a





  
  

  826
  
significant difference of ratios across Hyaline and Infected





  
  

  827
  
regions.</p>





  
  

  828
  
<pre class="r watch-out"><code>fisher.test(S2_table, alternative = &quot;two.sided&quot;)</code></pre>





  
  

  829
  
<pre><code>





  
  

  830
  
    Fisher&#39;s Exact Test for Count Data





  
  

  831
  





  
  

  832
  
data:  S2_table





  
  

  833
  
p-value &lt; 2.2e-16





  
  

  834
  
alternative hypothesis: true odds ratio is not equal to 1





  
  

  835
  
95 percent confidence interval:





  
  

  836
  
 0.5382305 0.5941683





  
  

  837
  
sample estimates:





  
  

  838
  
odds ratio 





  
  

  839
  
 0.5655696 </code></pre>





  
  

  840
  
</div>





  
  

  841
  
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  842
  





  
  

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